In a multiple antenna system using spatial multiplexing, a transmitter may transmit different channel-coded signals simultaneously through a plurality of transmission antennas, and a receiver may receive a signal in which transmission signals carrying different information transmitted from the transmitter through the plurality of transmission antennas are combined in space. Thus, the receiver needs to separate the spatially multiplexed signals and generate soft-decision values as a channel decoder input by soft-deciding channel-coded signals. In order generate the soft-decision values, the receiver needs a plurality of candidate symbol vectors. Accordingly, the receiver needs to detect the candidate symbol vectors with minimal complexity.
According to the related art, an algorithm that detects candidate symbol vectors is Maximum Likelihood Detection (MLD). MLD offers optimum performance but is very complex. In this context, tree search-based reception algorithms having low complexity without much performance degradation relative to MLD, such as, for example, List Sphere Decoding (LSD) and QR Decomposition-M algorithm (QRD-M) have been proposed. However, even if a tree search scheme is used to detect the candidate symbol vectors, a large volume of computation is still required to achieve performance approximate to the performance of MLD. Moreover, a detection time varies depending on a channel state or a noise magnitude in LSD and a sorting algorithm is needed for tree search in QRD-M.
In most multiple antenna systems using spatial multiplexing according to the related art, a receiver calculates soft-decision values of candidate symbol vectors by max-log approximation in order to reduce computation complexity. The use of max-log approximation may simplify the design problem of a receiver that generates soft-decision values to the design problem of a receiver that generates hard-decision values. With the use of a major technique for generating hard-decision information, Sphere Detection (SD), a receiver that generates soft-decision values with the same performance and low complexity relative to MLD may be designed. Nonetheless, SD still has high complexity and a detection time varies due to computation complexity depending on a channel state or a noise magnitude.
The above information is presented as background information only to assist with an understanding of the present disclosure. No determination has been made, and no assertion is made, as to whether any of the above might be applicable as prior art with regard to the present disclosure.